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Application of optimization model based on neural network in Softening Slope Stability by Strong Rainfall Infiltration

机译:基于神经网络的优化模型在强大的降雨渗透下柔软边坡稳定性

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Through analyzing main affecting factors of the artificial neural network model, the optimization model is established and the optimization parameters is obtain based on the method of momentum and self-adaptive of learn rate, This optimize artificial neural network is not only set up with the limited training samples, but also can improve the operating speed and study efficiency. The optimization mode of the of flood prediction of Slope Stability is build by Bedrock Softening under the Condition of Strong Rainfall Infiltration, it shows that its precision is high, and its computation is simple. The method by optimization neural network, which applied to bedrock strength decreases forecasting under the Condition of Strong Rainfall Infiltration, provides a new attempt for Prediction analysis and prove to be feasible and effective for practical experience in complex system engineering of bedrock Slope Stability.
机译:通过分析人工神经网络模型的主要影响因素,建立了优化模型,并基于学习速率的动量和自适应方法获得优化参数,这一优化人工神经网络不仅与有限的设置训练样本,也可以提高运行速度和学习效率。洪水稳定性洪水预测的优化模式由基岩软化在强大的降雨渗透状态下,表明其精度高,其计算很简单。优化神经网络的方法,其应用于基岩强度的预测降低了强大的降雨渗透的条件,提供了一种预测分析的新尝试,并证明了基于基岩边坡稳定性复杂系统工程中的实践经验。

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